Latest AI and machine learning research in nuclear medicine for healthcare professionals.
BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumulative radiation dose, particularly from the CT component. We propose SnapPET, a CT-sparing deep learning-based framework that uses 2D ultra-short non-attenuation-corrected (NAC) PET (Maximum intensity projection) MIP images to generate 2D high-qualit...
BACKGROUND: Positron emission tomography (PET) is a key tool for quantitative brain imaging, but its image quality and quantitative reliability are strongly dependent on injected radiotracer activity and acquisition time. Reducing injected dose or acquisition time lowers radiation exposure but increases image noise, while partial volume effects (PVE) further degrade signal accuracy, especially in ...
This study evaluates large language models (LLMs) for information extraction from French PET/CT reports related to cognitive impairment, focusing on d...
BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging...
Cardiac sarcoidosis (CS) is a clinically heterogeneous disorder associated with significant morbidity and mortality, including heart failure, conducti...
The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories...
BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...
OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography...
PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...
BACKGROUND: Perforation following chemotherapy in gastrointestinal lymphoma (PFCGL) is a rare but severe and life-threatening complication. Early pre-...
BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...
Neoadjuvant chemotherapy is a standard clinical practice for tumor downsizing in breast cancer, with [Formula: see text]F-FDG Positron Emission Tomogr...
BACKGROUND: Selective internal radiation therapy (SIRT) increasingly relies on accurate magnetic resonance imaging (MRI) to computed tomography (CT) r...
Whole-body bone scintigraphy is pivotal for skeletal evaluation in oncological monitoring, yet the unstructured nature of clinical reports impedes eff...
PURPOSE: To investigate the feasibility of non-invasively identifying bone marrow involvement (BMI) in follicular lymphoma (FL) using baseline 18F-FDG...
PURPOSES: To develop a deep learning model for automated metabolic tumor volume (MTV) delineation on routine computed tomography (CT) without positron...
Objective.Quantitative analysis of dynamic positron emission tomography (PET) scans requires knowledge of the arterial input function (AIF). Existing ...
Continuous depth-of-interaction (cDOI) detectors enable single-ended readout in positron emission tomography (PET) by encoding the interaction depth i...
Patch-wise learning is a common strategy for training neural networks on large-scale dense prediction problems, yet existing approaches assume uniform...
A decade has passed since the groundbreaking work by Defrise et al. (2012), which demonstrated that TOF PET imaging is self-correcting for a variety o...